Water-wind-light scene stochastic simulation method and system considering space-time correlation

Abstract

The invention belongs to the related technical field of electric power scene simulation, and discloses a water, wind and light scene stochastic simulation method and system considering space-time correlation, and the method comprises the steps: collecting historical data of water, wind and light resources; determining a scene simulation sequence and fitting edge distribution of each variable in any time period; for the first resource, constructing first condition distribution of the t time period variable when the t-1 time period variable is known through a two-dimensional Copula function; according to the first condition distribution, acquiring an analog value of the t time period variable; for any one of the second resource and the third resource, constructing second condition distribution of the t time period variable of the any resource through a three-dimensional Copula function when the t time period variable of the previous resource and the t-1 time period variable of the any resource are known; and according to the second condition distribution, acquiring an analog value of the variable in the t time period of the any resource. According to the method, correlation characteristics in different resource spaces are integrated, and multiple uncertainties of water, wind and light resources can be more comprehensively simulated and analyzed.

Description

Technical Field

[0001] The present invention belongs to the technical field related to power scene simulation, and more specifically, relates to a random simulation method and system for water, wind and light scenes considering time and space correlation. Background Art

[0002] In recent years, the domestic water, wind and solar energy complementarity has developed rapidly. The large-scale access to renewable energy such as hydropower, wind power and photovoltaics has provided broad prospects for the efficient use of clean energy and the low-carbonization of the power system. However, the integrated development of water, wind and solar energy still faces many challenges in this process, especially the volatility of water, wind and solar resources in time and space and their interaction, which leads to significant uncertainty in integrated development. How to effectively evaluate and quantify these uncertainties has become a key link in further promoting the development of water, wind and solar energy complementarity.

[0003] To solve this problem, it is urgent to build a method system that can scientifically describe multiple uncertainties, and stochastic simulation technology is one of the effective means to deal with uncertainty analysis. Through stochastic simulation, different scenarios that take into account the temporal and spatial changes of water, wind, and light resources can be generated, thereby converting uncertainty problems into quantifiable research objects. However, traditional stochastic simulation methods mostly focus on the time correlation analysis of a single energy source, and it is difficult to fully characterize the complex interrelationships between water, wind, and light resources at different time scales and spatial regions, and there are limitations in describing the uncertainty of multi-source coupling. Summary of the invention

[0004] In response to the above defects or improvement needs of the prior art, the present invention provides a random simulation method and system for water, wind and light scenes taking into account the temporal and spatial correlation, which is used to solve the problem that traditional random simulation methods focus on the time correlation analysis of a single energy source and have limitations in describing the uncertainty of multi-source coupling.

[0005] To achieve the above object, according to one aspect of the present invention, a method for stochastic simulation of water-landscape scenes considering temporal and spatial correlation is provided, comprising: S1, collect historical data of water resource runoff, wind resource wind power output and light resource photovoltaic output; calculate the relevant information between water, wind and light resources according to the historical data, and determine the order of water, wind and light scene simulation according to the relevant information, which is the first resource, the second resource and the third resource in order; and use the kernel density estimation method to fit the variables of water, wind and light resources in any period according to the historical data. t The marginal distribution of S2, for the first resource, using the two-dimensional Copula function t Time period variables and t -1 period marginal distribution construction t Time period variables andt -1 Two-dimensional joint distribution of period variables, obtain the known t -1 period variable t The first conditional distribution of the period variable; According to the first conditional distribution and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated value of the period variable; S3, for any resource of the second resource and the third resource, utilize the previous resource through the three-dimensional Copula function t Time period variable, any resource t -1 period variable and any resource t The marginal distribution of the period variable builds the previous resource t Time period variable, any resource t -1 period variable and any resource t Three-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is associated with any resource t -1 period variable when any resource t The second conditional distribution of the period variable; According to the second condition distribution, the previous resource t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated value of the period variable.

[0006] According to the random simulation method of water, wind and light scenes considering time and space correlation provided by the present invention, the historical data of water, wind and light resources in S1 are M OK N Matrix of columns Q : ; In the formula, Q For many years of historical data of resources, M is the number of years, , N Determined according to the target time scale.

[0007] According to the random simulation method of water, wind and light scenes considering spatiotemporal correlation provided by the present invention, in S1, the relevant information between water, wind and light resources is calculated according to historical data, and the order of water, wind and light scene simulation is determined according to the relevant information, specifically including: Calculate the mutual information between the water, wind and light resources according to historical data, and obtain the mutual information value between the water, wind and light resources; Calculate the sum of the mutual information values ​​between any resource and other resources, obtain the comprehensive mutual information index of any resource, and determine the order of simulation of the three resource scenarios based on the comprehensive mutual information index.

[0008] According to the random simulation method of water, scenery and light scenes considering time and space correlation provided by the present invention, for the first resource in S2, it is known that t -1 period variable t The first conditional distribution of the period variable Specifically: ; Where: The first resource t -1 period variable; The first resource t Period variables; C is the selected Copula function type; , Respectively represent the first resource t -1 period variable and t Marginal distribution of the period variable.

[0009] According to the random simulation method of water, scenery and light scenes considering time and space correlation provided by the present invention, in S2, according to the first condition distribution and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated values ​​of the time period variables include: Get a random number between 0 and 1 as the first conditional distribution value, and t -1 period variable marginal distribution value, get t Marginal distribution values ​​of the time period variable; according to t The marginal distribution values ​​of the time period variables and t Marginal distribution of time period variables, obtain t The simulated value of the period variable; When obtaining the first simulation value of the first resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it according to the average value of the variable information corresponding to the maximum time period in the historical data of the first resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t -1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

[0010] According to the random simulation method for water-landscape scenes considering spatiotemporal correlation provided by the present invention, obtaining the second conditional distribution in S3 specifically includes: Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t -1 period variable marginal distribution builds previous resource t The time period variable is associated with any resource t -2D joint distribution of variables in period 1, obtaining known previous resources t The time period variable is any resource t -1 period variable's third conditional distribution; Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t The marginal distribution of the period variable builds the previous resource t The time period variable is associated with any resource t Two-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is any resource t The fourth conditional distribution of the period variable; The third conditional distribution and the fourth conditional distribution are substituted into the formula of the second conditional distribution to convert the second conditional distribution into a function of the third conditional distribution and the fourth conditional distribution.

[0011] According to the random simulation method of water, scenery and light scenes considering time and space correlation provided by the present invention, S3 for any resource of the second resource and the third resource, it is known that the previous resource t The time period variable is associated with any resource t -1 period variable when any resource t Second conditional distribution of the period variable Specifically: ; ; ; Where: For any resource in the second resource or the third resource t Period variables; For any resource in the second resource or the third resource t -1 period variable; For the previous resource t Period variables; Indicates the previous resource t marginal distribution of period variables; , Respectively represents any resource in the second resource and the third resource t-1 period variable and t Marginal distribution of a time period variable: f is the probability density function of the variable, c is the probability density function of the Copula function; Indicates known u 2 conditional copula function.

[0012] According to the random simulation method of water, scenery and light scenes considering time and space correlation provided by the present invention, in S3, according to the second condition distribution, the previous resource t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated values ​​of the time period variables include: According to the previous resource t The marginal distribution value of the time period variable, any resource t -1 The marginal distribution value of the time period variable and the third conditional distribution obtain the third conditional distribution value; Obtain a random number between 0 and 1 as a second conditional distribution value, and obtain a fourth conditional distribution value according to the second conditional distribution value, the second conditional distribution, and the third conditional distribution value; According to the previous resource t The marginal distribution value of the time period variable, the fourth conditional distribution and the fourth conditional distribution value, obtain the any resource t Marginal distribution values ​​of the time period variable; According to any resource t The marginal distribution value and marginal distribution of the time period variable, obtain any resource t The simulated value of the period variable.

[0013] According to the random simulation method of water, scenery and light scenes considering time and space correlation provided by the present invention, any resource is obtained in S3 t The simulated values ​​of the time period variables also include: Previous resource t The marginal distribution value of the time period variable is based on the previous resource t The simulated values ​​of the time period variables and the marginal distribution calculations are obtained; When obtaining the first simulation value of any resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it based on the average value of the variable information corresponding to the maximum time period in the historical data of any resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t-1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

[0014] According to another aspect of the present invention, a random simulation system for water, landscape and light scenes considering time and space correlation is provided, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing any of the above-mentioned random simulation methods for water, landscape and light scenes considering time and space correlation when executing the computer program.

[0015] In general, compared with the prior art, the above technical solutions conceived by the present invention provide a random simulation method and system for water, scenery and light scenes taking into account temporal and spatial correlation: 1. When conducting scenario simulation, a three-dimensional Copula model is used to combine the time series characteristics of water, wind, and light resources with spatial correlation. While considering the time series characteristics of water, wind, and light resources, the spatial correlation characteristics between different resources can be further integrated, which is conducive to comprehensively describing the complex relationships between water, wind, and light resources in different time scales and spatial regions, thereby more comprehensively simulating and analyzing the multiple uncertainties of water, wind, and light resources. This method provides a solid theoretical basis and technical support for the optimal scheduling and efficient coordination of water, wind, and light multi-energy complementarity; 2. Compared with the traditional method that is limited to the time correlation analysis of a single resource, this method describes the spatial correlation characteristics between different resources through three-dimensional joint distribution, realizes the dimensionality reduction processing of the complex relationship between multi-source data, and greatly improves the ability to characterize multiple uncertainties, thus providing a more comprehensive and accurate model support for the scheduling optimization of water, wind and solar multi-energy complementarity; 3. The three-dimensional Copula function involved converts the second conditional distribution into a function of the third and fourth conditional distributions through the concept of conditional distribution, that is, it reduces the three-dimensional conditional distribution into multiple two-dimensional Copula functions, effectively avoiding the difficulties of constructing high-dimensional models and estimating high-dimensional model parameters, and effectively describing the spatiotemporal correlation of water, wind and light resources in a relatively simple way. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the random simulation method of water, scenery and light scenes considering the temporal and spatial correlation provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0018] See also Figure 1 The embodiment of the present invention provides a random simulation method for water-landscape scenes considering time-space correlation, and the random simulation method for water-landscape scenes includes: S1, collect historical data of water resource runoff, wind resource wind power output and light resource photovoltaic output; calculate the relevant information between water, wind and light resources according to the historical data, and determine the order of water, wind and light scene simulation according to the relevant information, which is the first resource, the second resource and the third resource in order; and use the kernel density estimation method to fit the variables of water, wind and light resources in any period according to the historical data. t The marginal distribution of S2, for the first resource, using the two-dimensional Copula function t Time period variables and t -1 period marginal distribution construction t Time period variables and t -1 Two-dimensional joint distribution of period variables, obtain the known t -1 period variable t The first conditional distribution of the period variable; According to the first conditional distribution and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated value of the period variable; S3, for any resource of the second resource and the third resource, utilize the previous resource through the three-dimensional Copula function t Time period variable, any resource t -1 period variable and any resource t The marginal distribution of the period variable builds the previous resource t Time period variable, any resource t -1 period variable and any resource t Three-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is associated with any resource t -1 period variable when any resource t The second conditional distribution of the period variable; According to the second condition distribution, the previous resource t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated value of the period variable.

[0019] Specifically, the historical data of water, wind and solar resources in S1, i.e. the historical measured water, wind and solar data, are M OK N Matrix of columns Q :

[0020] In the formula, Q For many years of historical data of resources, M is the number of years, , N Determined according to the target time scale; if it is daily scale data, N is 365; if it is ten-day scale data, N is 36; if it is monthly scale data, N is 12. The kernel density estimation method is used to fit the marginal distribution of each variable of water, wind and light in each period, that is, each variable is obtained N A marginal distribution.

[0021] In some specific embodiments, in S1, the relevant information between the water, wind and light resources is calculated according to historical data, and the order of water, wind and light scene simulation is determined according to the relevant information, which specifically includes: Calculate the mutual information between the water, wind and light resources according to historical data, use the mutual information to determine the relevant information between the water, wind and light resources, and obtain the mutual information value between the water, wind and light resources; Calculate the sum of the mutual information values ​​between any resource and other resources, obtain the comprehensive mutual information index of any resource, and determine the order of simulation of the three resource scenarios based on the comprehensive mutual information index.

[0022] The mutual information value between water, wind and light resources is as follows:

[0023] In the formula, is a random variable X and Y (e.g. mutual information value between water and wind, wind and light, light and water), x and y is a random variable X and Y observations, which represent specific data points in a random experiment or dataset. for x and y The joint probability distribution of They are x and y The marginal probability distribution of .

[0024] The comprehensive mutual information index of resources indicates the overall correlation between a resource and the other two resources. The order of scene generation is determined according to the value of the comprehensive mutual information index of each resource. The larger the comprehensive mutual information index, the higher the priority. The formula of the comprehensive mutual information index is as follows:

[0025] In the formula, is a random variable X The comprehensive mutual information index.

[0026] In some other specific embodiments, in S1, the relevant information between the water, wind and light resources is calculated according to the historical data, and the order of the water, wind and light scene simulation is determined according to the relevant information, specifically including: using the Kendall correlation coefficient to calculate the correlation degree between the water, wind and light resources, and obtaining their respective correlation coefficients; Taking a certain resource as the leading one, add the absolute value of the correlation coefficient between it and the other two resources to obtain the comprehensive correlation index of the resource; determine the order of random simulation of water, wind, and light resources according to the size of the comprehensive correlation index of each resource, and the larger the index, the higher the priority. This embodiment proposes a variety of methods for determining the simulation order of water, wind, and light scenes, which can be flexibly selected according to actual conditions and are not specifically limited.

[0027] Specifically, for the first resource in S2, it is known that t -1 period variable t The first conditional distribution of the period variable Specifically:

[0028] Where: The first resource t -1 period variable; The first resource t Period variables; C is the selected Copula function type; , Respectively represent the first resource t -1 period variable and t Marginal distribution of the period variable.

[0029] Further, in S2, according to the first condition distribution and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated values ​​of the time period variables include: Get a random number between 0 and 1 as the first conditional distribution value, and t-1 period variable marginal distribution value, get t Marginal distribution values ​​of the time period variable; according to t The marginal distribution values ​​of the time period variables and t Marginal distribution of time period variables, obtain t The simulated value of the period variable; When obtaining the first simulation value of the first resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it according to the average value of the variable information corresponding to the maximum time period in the historical data of the first resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t -1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

[0030] In some specific embodiments, obtaining the second conditional distribution in S3 specifically includes: Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t -1 period variable marginal distribution builds previous resource t The time period variable is associated with any resource t -2D joint distribution of variables in period 1, obtaining known previous resources t The time period variable is any resource t -1 period variable's third conditional distribution; Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t The marginal distribution of the period variable builds the previous resource t The time period variable is associated with any resource t Two-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is any resource t The fourth conditional distribution of the period variable; The third conditional distribution and the fourth conditional distribution are substituted into the formula of the second conditional distribution to convert the second conditional distribution into a function of the third conditional distribution and the fourth conditional distribution.

[0031] Specifically, S3 knows that for any resource of the second resource and the third resource, the previous resource t The time period variable is associated with any resource t -1 period variable when any resource t Second conditional distribution of the period variable Specifically: ; ; ;

[0032] Where: For any resource in the second resource or the third resource t Period variables; For any resource in the second resource or the third resource t -1 period variable; For the previous resource t Period variables; Indicates the previous resource t marginal distribution of period variables; , Respectively represents any resource in the second resource and the third resource t -1 period variable and t Marginal distribution of a time period variable: f is the probability density function of the variable, c is the probability density function of the Copula function; , Indicates known u 2 conditional copula function.

[0033] Further, in S3, according to the second condition distribution, the previous resource t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated values ​​of the time period variables include: According to the previous resource t The marginal distribution value of the time period variable, any resource t -1 period variable marginal distribution value and the third conditional distribution Get the third conditional distribution value; Obtain a random number between 0 and 1 as a second conditional distribution value, and obtain a fourth conditional distribution value according to the second conditional distribution value, the second conditional distribution, and the third conditional distribution value; According to the previous resource t The marginal distribution value of the time period variable, the fourth conditional distribution And the fourth conditional distribution value, obtain any resource t Marginal distribution values ​​of the time period variable; According to any resource t The marginal distribution value and marginal distribution of the time period variable, obtain any resource t The simulated value of the period variable.

[0034] Further, any resource is obtained from S3 t The simulated values ​​of the time period variables also include: Previous resource t The marginal distribution value of the time period variable is based on the previous resource t The simulated values ​​of the time period variables and the marginal distribution calculations are obtained; When obtaining the first simulation value of any resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it based on the average value of the variable information corresponding to the maximum time period in the historical data of any resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t -1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

[0035] In some specific embodiments, in view of the defects of the prior art, this embodiment provides a water, wind and light random simulation method based on a three-dimensional Copula model according to the present invention, aiming to solve the problems that the existing random simulation method only considers the simulation of a single resource or the simulation of a single correlation. This embodiment provides a water, wind and light random simulation method based on a three-dimensional Copula model, comprising the following steps: Step 1: Collect water (runoff) h ), wind (wind power output w ), light (photoelectric output p ) The historical data of the three resources are used to calculate the correlation between water, wind and light, and the order of random simulation of water, wind and light is determined according to the calculation results (assuming w>p>h). t -1 period and t The variables for the time periods are h t-1 and h t , w t-1 and w t , p t-1 and p t .

[0036] Step 2: Based on the historical measured water, wind and light data, the kernel density estimation method is used to fit the marginal distribution of each water, wind and light variable in each time period.

[0037] Step 3: According to step 2 w The marginal distribution of the variable in each period is constructed using a two-dimensional Copula function w t andw t-1 The two-dimensional joint distribution of w t-1 hour w t Conditional distribution; get a random number between 0 and 1 e 1. According to e 1. w t-1 The marginal distribution values ​​and known w t-1 hour w t The conditional distribution of is known w t-1 hour w t The marginal distribution value of w t The marginal distribution value of w t The marginal distribution of w t The simulation value of w t As new w t-1 , until enough w Variable data; Step 4: According to step 2 w Variables and p The marginal distribution of the variable in each period is constructed using a two-dimensional Copula function w t and p t-1 The two-dimensional joint distribution of w t and p t The two-dimensional joint distribution of w t hour p t-1 The conditional distribution and known w t hour p t The conditional distribution of w t , p t-1 and p t The three-dimensional joint distribution of w t and p t-1 hour pt The conditional distribution of w t and p t-1 The marginal distribution values ​​and known w t hour p t-1 The conditional distribution of is known w t hour p t-1 Conditional distribution value; get a random number between 0 and 1 e 2. According to e 2. Known w t hour p t-1 The conditional distribution value and known w t and p t-1 hour p t The conditional distribution of is known w t hour p t The conditional distribution value of w t The marginal distribution value of w t hour p t The conditional distribution and known w t hour p t Calculation of conditional distribution value of p t The marginal distribution value of p t The marginal distribution value of p t The marginal distribution of p t The simulation value of p t As new p t-1 , until enough p Variable data; Step 5: According to step 2 p Variables and h The marginal distribution of the variable in each period is constructed using a two-dimensional Copula function p t and h t-1 The two-dimensional joint distribution of pt and h t The two-dimensional joint distribution of p t hour h t-1 The conditional distribution and known p t hour h t The conditional distribution of p t , h t-1 and h t The three-dimensional joint distribution of p t and h t-1 hour h t The conditional distribution of p t and h t-1 The marginal distribution values ​​and known p t hour h t-1 The conditional distribution of is known p t hour h t-1 Conditional distribution value; get a random number between 0 and 1 e 3. According to e 3. Known p t hour h t-1 The conditional distribution value and known p t and h t-1 hour h t The conditional distribution of is known p t hour h t The conditional distribution value of p t The marginal distribution value of h t hour h t The conditional distribution and known p t hour h t Calculation of conditional distribution value of h tThe marginal distribution value of h t The marginal distribution value of h t The marginal distribution of h t The simulation value of h t As new h t-1 , until enough h Variable data; Furthermore, step three includes: 31: The two-dimensional Copula function can connect the joint distribution of two variables with their respective marginal distribution functions. The two-dimensional joint distribution expression of two variables constructed by the two-dimensional Copula function is as follows:

[0038] Where: is the selected Copula function type; is the parameter of the Copula function, which is determined by the Kendall correlation coefficient and partial correlation coefficient; , Variables constructed X ,variable Y The marginal distribution of For variables X and variables Y The joint distribution of .

[0039] 32: Using two-dimensional Copula function to build w t and w t-1 The two-dimensional joint distribution of is expressed as:

[0040] Where: express w t and w t-1 The two-dimensional joint distribution of , Respectively w variable t -1 period and t The marginal distribution of the time period.

[0041] 33: Known w t-1 hour w t The conditional distribution of , the expression is:

[0042] Where: Indicates known u 1 conditional copula function.

[0043] When considering the number of years of simulation data required m After that, the expression is:

[0044] Where: For the m Year t The simulated value of the time period, For the m Year t -1 period of simulation value, Respectively m Year w variable t -1 period and t The marginal distribution value of the time period.

[0045] 34: Get a random number between 0 and 1 e 1. Order e 1 and Equal, calculated known of ;when m = 1 and t = 1, Use random number generation or w In historical data t = N The variable information at that time is averaged and then calculated based on the corresponding marginal distribution; 35: Yes Perform inverse change sampling to obtain analog values , the expression is:

[0046] 36: The calculated As new ;when t = N hour, m = m + 1, which means entering the next year's simulation; the new year's simulation t = 1, ; Repeat steps 34-36 until m Equal to the target number of years.

[0047] Furthermore, step four includes: 41: Using two-dimensional Copula function to build w t and p t-1 The two-dimensional joint distribution of w t and p t The two-dimensional joint distribution of is expressed as:

[0048] Where: express w t and p t-1 The two-dimensional joint distribution of express w t and p t The two-dimensional joint distribution of , Respectively p variable t -1 period and t The marginal distribution of the time period.

[0049] 42: Known w t hour p t-1 The conditional distribution of and known w t hour p t The conditional distribution of , the expression is:

[0050] Where: Indicates known u 2 conditional copula function.

[0051] 43: The three-dimensional Copula function can connect the joint distribution of three variables with their respective marginal distribution functions. The three-dimensional joint distribution of three variables constructed by the three-dimensional Copula function is expressed as follows:

[0052] Where: Variables constructed Z The marginal distribution of For variables X , Y and Z The joint distribution of .

[0053] 44: According to w t and p t-1 The two-dimensional joint distribution of w t and p t Two-dimensional joint distribution construction w t , p t-1 and p t The three-dimensional joint distribution of w t and p t-1 hour p t The conditional distribution of , the steps are:

[0054] In the formula: , , respectively represent the known w t hour p t-1 and p t The conditional distribution of ;

[0055] Where: is the probability density function of the variable, is the probability density function of the Copula function.

[0056] Therefore It can be written as: ; in, , ; Therefore It can be written as: ;

[0057] When considering the number of years of simulation data required m After that, the expression is: ;

[0058] Where: Respectively m Year w variable t Time period and p variable t -1 period and tThe marginal distribution value of the time period. Respectively m Year Known w t hour p t-1 and p t The conditional distribution value of .

[0059] 45: According to w t and p t-1 The marginal distribution values ​​and known w t hour p t-1 The conditional distribution of is known w t hour p t-1 The conditional distribution value of .

[0060] 46: Get a random number between 0 and 1 e 2. The basis e 2. Known w t hour p t-1 The conditional distribution value and known w t and p t-1 hour p t The conditional distribution of is known w t hour p t The conditional distribution value of , ,make e 2 and Equal, calculation ;when m = 1 and t = 1, Use random number generation or p In historical data t = N The variable information at that time is averaged and then calculated based on the corresponding marginal distribution; 47: According to w t The marginal distribution value of w t hour p t The conditional distribution and known w t hourp t Calculation of conditional distribution value of p t The marginal distribution value of , calculate , that is, calculation p t The marginal distribution value of .

[0061] 48: Yes Perform inverse change sampling to obtain analog values , the expression is: ;

[0062] 49: The calculated As new ;when t = N hour, m = m + 1, which means entering the next year's simulation; the new year's simulation t = 1, ; Repeat 46-49 until m Equal to the target number of years.

[0063] 410:41-49 Known w t These are the simulated values ​​in step 3. .

[0064] Furthermore, step five is similar to step four: w t , p t-1 and p t Replace with p t , h t-1 and h t Replace, follow the steps described in step 4 h Simulation of variables, the known p t All are the simulation values ​​in step 4 .

[0065] Furthermore, this embodiment provides a random simulation system for water, landscape and light scenes considering time and space correlation, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing any of the above-mentioned random simulation methods for water, landscape and light scenes considering time and space correlation when executing the computer program.

[0066] Taking the daily historical hydrological data of Xiluodu Hydropower Station and the daily historical wind speed and photovoltaic intensity in the concentrated area of ​​wind power and photovoltaic planned installed capacity in the vicinity of Xiluodu Hydropower Station as the implementation objects, the historical wind speed and photovoltaic intensity are converted into wind power output and photovoltaic output according to the planned installed capacity. The sequence lengths of runoff, wind power output and photovoltaic output are all from 1940 to 2021. The present invention is used to perform random simulation on the sequence, and each generates a simulation sequence of 1000 years in size. The embodiment will use statistical indicators: mean, standard deviation, skewness coefficient, kurtosis coefficient; time correlation index (Pearson correlation coefficient); spatial correlation index (average Kendall correlation coefficient absolute error) to measure the time correlation of simulation sequences and the spatial correlation between them.

[0067] Tables 1 to 3 respectively give the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) between the statistical characteristics and correlation coefficients of the daily-scale water, wind and light sequences simulated and generated by the method of the present invention and the two-dimensional Copula method and the statistical characteristics and correlation coefficients of the measured daily-scale water, wind and light sequences. Table 4 gives the spatial correlation index (average Kendall correlation coefficient absolute error) between the daily-scale wind-light, water-wind and water-light sequences simulated and generated by the method of the present invention and the two-dimensional Copula method. Among them, the smaller the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE), the higher the statistical characteristics and correlation between the simulated sequence and the measured sequence; the smaller the spatial correlation index (average Kendall correlation coefficient absolute error), the more accurately the simulated sequence can describe the spatial correlation characteristics between the resources; the bold numbers in Tables 1 to 4 indicate that the numerical value of this method is optimal; ; ; ; ; Among the comparison methods, the two-dimensional Copula method only considers the spatial correlation between resources. As can be seen from Tables 1 to 3, the simulation effect of the present invention in terms of statistical characteristics and time correlation is better than that of the comparison method. As can be seen from Table 4, the simulation effect of the present invention in terms of spatial correlation between wind-solar, water-wind, and water-solar sequences is better than that of the comparison method.

[0068] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A random simulation method for water-landscape scenes considering temporal and spatial correlation, characterized in that: include: S1, collect historical data of water resource runoff, wind resource wind power output and light resource photovoltaic output; calculate the relevant information between water, wind and light resources according to the historical data, and determine the order of water, wind and light scene simulation according to the relevant information, which is the first resource, the second resource and the third resource in order; and use the kernel density estimation method to fit the variables of water, wind and light resources in any period according to the historical data. t The marginal distribution of S2, for the first resource, using the two-dimensional Copula function t Time period variables and t -1 period marginal distribution construction t Time period variables and t -1 Two-dimensional joint distribution of period variables, obtain the known t -1 period variable t The first conditional distribution of the period variable; According to the first conditional distribution and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated value of the period variable; S3, for any resource of the second resource and the third resource, utilize the previous resource through the three-dimensional Copula function t Time period variable, any resource t -1 period variable and any resource t The marginal distribution of the period variable builds the previous resource t Time period variable, any resource t -1 period variable and any resource t Three-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is associated with any resource t -1 period variable when any resource t The second conditional distribution of the period variable; According to the second condition distribution, the previous resource t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated value of the period variable.

2. The random simulation method of water-landscape scene considering time-space correlation according to claim 1 is characterized in that: The historical data of water and wind resources in S1 are M OK N Matrix of columns Q : ； In the formula, Q For many years of historical data of resources, M is the number of years, , N Determined according to the target time scale.

3. The random simulation method of water-landscape scene considering time-space correlation according to claim 1 is characterized in that: In S1, the relevant information between water, wind and light resources is calculated based on historical data, and the order of water, wind and light scene simulation is determined based on the relevant information, specifically including: Calculate the mutual information between the water, wind and light resources according to historical data, and obtain the mutual information value between the water, wind and light resources; Calculate the sum of the mutual information values ​​between any resource and other resources, obtain the comprehensive mutual information index of any resource, and determine the order of simulation of the three resource scenarios based on the comprehensive mutual information index.

4. The random simulation method of water-landscape scene considering time-space correlation according to claim 1, characterized in that: In S2, for the first resource, it is known that t -1 period variable t The first conditional distribution of the period variable Specifically: ； Where: The first resource t -1 period variable; The first resource t Period variables; C is the selected Copula function type; , Respectively represent the first resource t -1 period variable and t Marginal distribution of the period variable.

5. The random simulation method for water-landscape scene considering time-space correlation according to any one of claims 1 to 4, characterized in that: S2 is distributed according to the first condition and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated values ​​of the time period variables, including: Get a random number between 0 and 1 as the first conditional distribution value, and t -1 period variable marginal distribution value, get t Marginal distribution values ​​of the time period variable; according to t The marginal distribution values ​​of the time period variables and t Marginal distribution of time period variables, obtain t The simulated value of the period variable; When obtaining the first simulation value of the first resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it according to the average value of the variable information corresponding to the maximum time period in the historical data of the first resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t -1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

6. The random simulation method for water-landscape scene considering time-space correlation according to any one of claims 1 to 4, characterized in that: The acquisition of the second conditional distribution in S3 specifically includes: Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t -1 period variable marginal distribution builds previous resource t The time period variable is associated with any resource t -2D joint distribution of variables in period 1, obtaining known previous resources t The time period variable is any resource t -1 period variable's third conditional distribution; Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t The marginal distribution of the period variable builds the previous resource t The time period variable is associated with any resource t Two-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is any resource t The fourth conditional distribution of the period variable; The third conditional distribution and the fourth conditional distribution are substituted into the formula of the second conditional distribution to convert the second conditional distribution into a function of the third conditional distribution and the fourth conditional distribution.

7. The random simulation method of water-landscape scene considering time-space correlation according to claim 6 is characterized in that: S3 For any resource in the second resource and the third resource, the previous resource is known t The time period variable is associated with any resource t -1 period variable when any resource t Second conditional distribution of the period variable Specifically: ； ； ； Where: For any resource in the second resource or the third resource t Period variables; For any resource in the second resource or the third resource t -1 period variable; For the previous resource t Period variables; Indicates the previous resource t marginal distribution of period variables; , Respectively represents any resource in the second resource and the third resource t -1 period variable and t Marginal distribution of a time period variable: f is the probability density function of the variable, c is the probability density function of the Copula function; Indicates known u 2 conditional copula function.

8. The method for stochastic simulation of water, scenery and light scenes considering temporal and spatial correlation according to claim 6, characterized in that: S3 distributes the previous resource according to the second condition t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated values ​​of the time period variables, including: According to the previous resource t The marginal distribution value of the time period variable, any resource t -1 The marginal distribution value of the time period variable and the third conditional distribution obtain the third conditional distribution value; Obtain a random number between 0 and 1 as a second conditional distribution value, and obtain a fourth conditional distribution value according to the second conditional distribution value, the second conditional distribution, and the third conditional distribution value; According to the previous resource t The marginal distribution value of the time period variable, the fourth conditional distribution and the fourth conditional distribution value, obtain the any resource t Marginal distribution values ​​of the time period variable; According to any resource t The marginal distribution value and marginal distribution of the time period variable, obtain any resource t The simulated value of the period variable.

9. The method for stochastic simulation of water, scenery and light scenes considering temporal and spatial correlation according to claim 8, characterized in that: Get any resource in S3 t The simulated values ​​of the time period variables also include: Previous resource t The marginal distribution value of the time period variable is based on the previous resource t The simulated values ​​of the time period variables and the marginal distribution calculations are obtained; When obtaining the first simulation value of any resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it based on the average value of the variable information corresponding to the maximum time period in the historical data of any resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t -1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

10. A random simulation system for water, scenery and light scenes considering temporal and spatial correlation, characterized in that: The system includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the random simulation method for water, landscape and light scenes considering time and space correlation described in any one of claims 1 to 9 is executed.

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